All things being equal: diversity in STEM
Authors: None The Lancet Digital Health
Journal: The Lancet Digital Health (2020)

March 8, 2020, marked International Women's Day. First observed in 1911 in Austria, Denmark, Germany, and Switzerland, International Women's Day is now celebrated the world over. This year also marks 25 years since the adoption of the Beijing Platform for Action—an agenda aimed at “removing all the obstacles to women's active participation in all spheres of public and private life”—and 5 years since the adoption of Sustainable Development Goal number 5: “achieve gender equality and empower all women and girls”. Yet, the reality is that gender equity across STEM is still in a parlous state. In the UK, women only accounted for 24% of the core STEM workforce and occupied just 16·4% of tech roles in 2019. In the USA, an audit of 250 biotech start-ups led by members of the Massachusetts Institute of Technology faculty revealed that fewer than 10% of these companies were founded by women. One response to this poor gender parity has been to encourage more girls to study STEM subjects in school. But encouragement is one thing; retention is another. Despite many women obtaining STEM degrees, few continue to work in the field in which they qualified, known as the so-called leaky pipeline. Retention requires not only greater representation, but also the overcoming of entrenched biases. Sexual harassment and bullying have forced many women out of STEM. Although in some places the gender pay gap has narrowed, in the USA women still earned 15% less than men in 2018. Further barriers include bias during the hiring process, difficulties reintegrating following a career break, and women's low confidence in their abilities despite their accomplishments, all of which can hinder career progression. Divides within STEM are not solely linked to gender. Less than 20% of NHS staff identify as black, Asian and minority ethnic (BAME). Within the top tech firms in the UK, over 70% of boards and senior executive teams do not have a BAME member; in fact, women of BAME backgrounds only make up around 2% of boards and senior executive teams. Unfortunately, little data are available on the representation of BAME individuals in health tech, meaning we do not really know the extent of the problem. If we are unable to quantify the existing gaps, we cannot effectively narrow them. Have these deficiencies in diversity had a direct effect on digital health outputs? In short, yes. Algorithms are frequently found to contain inbuilt bias rather than being objective tools. Virtual general practitioner chatbots can offer different diagnoses for the same symptoms, with the only differing factor being gender. Regarding ethnicity, a commercial algorithm commonly used in the US health-care system was shown to significantly discriminate against black patients by assigning them the same level of algorithmic-predicted risk as white patients with comparatively less illness, suggesting that fewer black patients are assigned the additional care they need. Genomic datasets are often predominantly of European descent, meaning new associations between genetic variants and diseases in those of non-European ancestry are being missed. More diverse STEM leadership could help to ensure that whole patient groups are not neglected, and thus prevent such health and research inequity. What is being done to change this? Many organisations have been formed to inspire more women—from a young age—to take an interest in STEM subjects. Various initiatives have been set up to boost digital health skills in young girls and BAME individuals, so as to support them and bolster their numbers in the tech industry. Some companies employ Equality, Diversity, and Inclusion Officers whose express role is to foster and monitor best practice in these areas. On a global scale, the STEM and Gender Advancement project run by UNESCO aims to reduce the STEM gender gap at the education, researcher, and policy level. Likewise, One HealthTech is also taking a global stance to support and promote under-represented groups within health innovation. In the realm of digital health, it is the case that we do not know what we do not know. As the issue of bias in datasets and algorithms continues to be raised, these inequalities can hopefully be addressed. To aid these efforts, we not only need the support of dedicated groups and policies, but also need more data to quantify the problem of parity and to enable us to take more informed and decisive action. STEM is not for the chosen few; it is for everyone and it affects everyone. For more on low retention of women in STEM see https://edition.cnn.com/2019/04/12/us/scientist-women-retention-sci-trnd/index.htmlFor more on racial bias in risk algorithms see Science 2019; 366: 447–53For more on poor diversity in genomic datasets see Nature 2016; 538: 161–64 For more on low retention of women in STEM see https://edition.cnn.com/2019/04/12/us/scientist-women-retention-sci-trnd/index.html For more on racial bias in risk algorithms see Science 2019; 366: 447–53 For more on poor diversity in genomic datasets see Nature 2016; 538: 161–64